Wearable speech input-based vision to audio interpreter

ABSTRACT

An eyewear device with camera-based compensation that improves the user experience for user&#39;s having partial blindness or complete blindness. The camera-based compensation determines features, such as objects, and then converts the determined objects to audio that is indicative of the objects and that is perceptible to the eyewear user. The camera-based compensation may use a region-based convolutional neural network (RCNN) to generate a feature map including text that is indicative of objects in images captured by a camera. The feature map is then processed through a speech to audio algorithm featuring a natural language processor to generate audio indicative of the objects in the processed images.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 62/887,134 entitled WEARABLE SPEECH INPUT-BASED VISION TO AUDIOINTERPRETER, filed on Aug. 15, 2019, the contents of which areincorporated fully herein by reference.

TECHNICAL FIELD

The present subject matter relates to an eyewear device, e.g., smartglasses.

BACKGROUND

Portable eyewear devices, such as smart glasses, headwear, and headgearavailable today integrate cameras and see-through displays. Users withless than perfect vision may have issues with using these eyeweardevices.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawing figures depict one or more implementations, by way ofexample only, not by way of limitations. In the figures, like referencenumerals refer to the same or similar elements.

FIG. 1A is a side view of an example hardware configuration of aneyewear device, which shows a right optical assembly with an imagedisplay, and field of view adjustments are applied to a user interfacepresented on the image display based on detected head or eye movement bya user;

FIG. 1B is a top cross-sectional view of a temple of the eyewear deviceof FIG. 1A depicting a visible light camera, a head movement tracker fortracking the head movement of the user of the eyewear device, and acircuit board;

FIG. 2A is a rear view of an example hardware configuration of aneyewear device, which includes an eye scanner on a frame, for use in asystem for identifying a user of the eyewear device;

FIG. 2B is a rear view of an example hardware configuration of anothereyewear device, which includes an eye scanner on a temple, for use in asystem for identifying a user of the eyewear device;

FIGS. 2C and 2D are rear views of example hardware configurations of theeyewear device, including two different types of image displays.

FIG. 3 shows a rear perspective view of the eyewear device of FIG. 2Adepicting an infrared emitter, an infrared camera, a frame front, aframe back, and a circuit board;

FIG. 4 is a cross-sectional view taken through the infrared emitter andthe frame of the eyewear device of FIG. 3 ;

FIG. 5 illustrates detecting eye gaze direction;

FIG. 6 illustrates detecting eye position;

FIG. 7 depicts an example of visible light captured by the left visiblelight camera as a left raw image and visible light captured by the rightvisible light camera as a right raw image;

FIG. 8A illustrates the camera-based compensation system that identifiesobjects in an image, such as a cowboy, converts identified objects totext, and then converts the text to audio that is indicative of theidentified objects in the image;

FIG. 8B illustrates an image, such as a restaurant menu, with sectionsthat can be instructed via speech to be processed and read aloud to auser;

FIG. 9 illustrates a block diagram of electronic components of theeyewear device; and

FIG. 10 is a flowchart of the operation of the eyewear device.

DETAILED DESCRIPTION

Users with nearsightedness, farsightedness, color blindness or blindnessgenerally may have trouble using eyewear devices, including those withsee-through displays. This disclosure includes examples of eyewear withcamera-based compensation that improves the user experience of eyeweardevices for user's having partial blindness or complete blindness. Tocompensate for blindness, the camera-based compensation receives speechfrom a user with instructions to determine generate audio indicative ofobjects in an image, and then generates audio based on the speech thatis indicative of the objects and that is perceptible to the eyewearuser.

For example, the camera-based compensation uses a region-basedconvolutional neural network (RCNN) to generate a feature map includingtext that is indicative of objects in images captured by a camera.Relevant text of the feature map is then processed through a text tospeech algorithm featuring a natural language processor to generateaudio indicative of the objects in the processed images. In anotherexample, the camera-based compensation uses the RCNN to process speechand read aloud information based on the speech, such as reading thedaily specials of a restaurant menu.

Additional objects, advantages and novel features of the examples willbe set forth in part in the description which follows, and in part willbecome apparent to those skilled in the art upon examination of thefollowing and the accompanying drawings or may be learned by productionor operation of the examples. The objects and advantages of the presentsubject matter may be realized and attained by means of themethodologies, instrumentalities and combinations particularly pointedout in the appended claims.

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, components,and circuitry have been described at a relatively high-level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

The term “coupled” as used herein refers to any logical, optical,physical or electrical connection, link or the like by which signals orlight produced or supplied by one system element are imparted to anothercoupled element. Unless described otherwise, coupled elements or devicesare not necessarily directly connected to one another and may beseparated by intermediate components, elements or communication mediathat may modify, manipulate or carry the light or signals.

The orientations of the eyewear device, associated components and anycomplete devices incorporating an eye scanner and camera such as shownin any of the drawings, are given by way of example only, forillustration and discussion purposes. In operation for a particularvariable optical processing application, the eyewear device may beoriented in any other direction suitable to the particular applicationof the eyewear device, for example up, down, sideways, or any otherorientation. Also, to the extent used herein, any directional term, suchas front, rear, inwards, outwards, towards, left, right, lateral,longitudinal, up, down, upper, lower, top, bottom and side, are used byway of example only, and are not limiting as to direction or orientationof any optic or component of an optic constructed as otherwise describedherein.

Reference now is made in detail to the examples illustrated in theaccompanying drawings and discussed below.

FIG. 1A is a side view of an example hardware configuration of aneyewear device 100, which includes a right optical assembly 180B with animage display 180D (FIG. 2A). Eyewear device 100 includes multiplevisible light cameras 114A-B (FIG. 7 ) that form a stereo camera, ofwhich the right visible light camera 114B is located on a right temple110B.

The left and right visible light cameras 114A-B have an image sensorthat is sensitive to the visible light range wavelength. Each of thevisible light cameras 114A-B have a different frontward facing angle ofcoverage, for example, visible light camera 114B has the depicted angleof coverage 111B. The angle of coverage is an angle range which theimage sensor of the visible light camera 114A-B picks up electromagneticradiation and generates images. Examples of such visible lights camera114A-B include a high-resolution complementary metal-oxide-semiconductor(CMOS) image sensor and a video graphic array (VGA) camera, such as 640p(e.g., 640×480 pixels for a total of 0.3 megapixels), 720p, 1080p, orgreater. Image sensor data from the visible light cameras 114A-B arecaptured along with geolocation data, digitized by an image processor,and stored in a memory.

To provide stereoscopic vision, visible light cameras 114A-B may becoupled to an image processor (element 912 of FIG. 9 ) for digitalprocessing along with a timestamp in which the image of the scene iscaptured. Image processor 912 includes circuitry to receive signals fromthe visible light camera 114A-B and process those signals from thevisible light cameras 114A-B into a format suitable for storage in thememory (element 934 of FIG. 9 ). The timestamp can be added by the imageprocessor 912 or other processor, which controls operation of thevisible light cameras 114A-B. Visible light cameras 114A-B allow thestereo camera to simulate human binocular vision. Stereo cameras providethe ability to reproduce three-dimensional images (element 715 of FIG. 7) based on two captured images (elements 758A-B of FIG. 7 ) from thevisible light cameras 114A-B, respectively, having the same timestamp.Such three-dimensional images 715 allow for an immersive life-likeexperience, e.g., for virtual reality or video gaming. For stereoscopicvision, the pair of images 758A-B are generated at a given moment intime—one image for each of the left and right visible light cameras114A-B. When the pair of generated images 758A-B from the frontwardfacing angles of coverage 111A-B of the left and right visible lightcameras 114A-B are stitched together (e.g., by the image processor 912),depth perception is provided by the optical assembly 180A-B.

In an example, a user interface field of view adjustment system includesthe eyewear device 100. The eyewear device 100 includes a frame 105, aright temple 110B extending from a right lateral side 170B of the frame105, and a see-through image display 180D (FIGS. 2A-B) comprisingoptical assembly 180B to present a graphical user interface to a user.The eyewear device 100 includes the left visible light camera 114Aconnected to the frame 105 or the left temple 110A to capture a firstimage of the scene. Eyewear device 100 further includes the rightvisible light camera 114B connected to the frame 105 or the right temple110B to capture (e.g., simultaneously with the left visible light camera114A) a second image of the scene which partially overlaps the firstimage. Although not shown in FIGS. 1A-B, the user interface field ofview adjustment system further includes the processor 932 coupled to theeyewear device 100 and connected to the visible light cameras 114A-B,the memory 934 accessible to the processor 932, and programming in thememory 934, for example in the eyewear device 100 itself or another partof the user interface field of view adjustment system.

Although not shown in FIG. 1A, the eyewear device 100 also includes ahead movement tracker (element 109 of FIG. 1B) or an eye movementtracker (element 213 of FIG. 2B). Eyewear device 100 further includesthe see-through image displays 180C-D of optical assembly 180A-B forpresenting a sequence of displayed images, and an image display driver(element 942 of FIG. 9 ) coupled to the see-through image displays180C-D of optical assembly 180A-B to control the image displays 180C-Dof optical assembly 180A-B to present the sequence of displayed images715, which are described in further detail below. Eyewear device 100further includes the memory 934 and the processor 932 having access tothe image display driver 942 and the memory 934. Eyewear device 100further includes programming (element 934 of FIG. 9 ) in the memory.Execution of the programming by the processor 932 configures the eyeweardevice 100 to perform functions, including functions to present, via thesee-through image displays 180C-D, an initial displayed image of thesequence of displayed images, the initial displayed image having aninitial field of view corresponding to an initial head direction or aninitial eye gaze direction (element 230 of FIG. 5 ).

Execution of the programming by the processor 932 further configures theeyewear device 100 to detect movement of a user of the eyewear deviceby: (i) tracking, via the head movement tracker (element 109 of FIG.1B), a head movement of a head of the user, or (ii) tracking, via an eyemovement tracker (element 213 of FIG. 2B, FIG. 5 ), an eye movement ofan eye of the user of the eyewear device 100. Execution of theprogramming by the processor 932 further configures the eyewear device100 to determine a field of view adjustment to the initial field of viewof the initial displayed image based on the detected movement of theuser. The field of view adjustment includes a successive field of viewcorresponding to a successive head direction or a successive eyedirection. Execution of the programming by the processor 932 furtherconfigures the eyewear device 100 to generate a successive displayedimage of the sequence of displayed images based on the field of viewadjustment. Execution of the programming by the processor 932 furtherconfigures the eyewear device 100 to present, via the see-through imagedisplays 180C-D of the optical assembly 180A-B, the successive displayedimages.

FIG. 1B is a top cross-sectional view of the temple of the eyeweardevice 100 of FIG. 1A depicting the right visible light camera 114B, ahead movement tracker 109, and a circuit board. Construction andplacement of the left visible light camera 114A is substantially similarto the right visible light camera 114B, except the connections andcoupling are on the left lateral side 170A. As shown, the eyewear device100 includes the right visible light camera 114B and a circuit board,which may be a flexible printed circuit board (PCB) 140. The right hinge126B connects the right temple 110B to a right temple 125B of theeyewear device 100. In some examples, components of the right visiblelight camera 114B, the flexible PCB 140, or other electrical connectorsor contacts may be located on the right temple 125B or the right hinge126B.

As shown, eyewear device 100 has a head movement tracker 109, whichincludes, for example, an inertial measurement unit (IMU). An inertialmeasurement unit is an electronic device that measures and reports abody's specific force, angular rate, and sometimes the magnetic fieldsurrounding the body, using a combination of accelerometers andgyroscopes, sometimes also magnetometers. The inertial measurement unitworks by detecting linear acceleration using one or more accelerometersand rotational rate using one or more gyroscopes. Typical configurationsof inertial measurement units contain one accelerometer, gyro, andmagnetometer per axis for each of the three axes: horizontal axis forleft-right movement (X), vertical axis (Y) for top-bottom movement, anddepth or distance axis for up-down movement (Z). The accelerometerdetects the gravity vector. The magnetometer defines the rotation in themagnetic field (e.g., facing south, north, etc.) like a compass whichgenerates a heading reference. The three accelerometers to detectacceleration along the horizontal, vertical, and depth axis definedabove, which can be defined relative to the ground, the eyewear device100, or the user wearing the eyewear device 100.

Eyewear device 100 detects movement of the user of the eyewear device100 by tracking, via the head movement tracker 109, the head movement ofthe head of the user. The head movement includes a variation of headdirection on a horizontal axis, a vertical axis, or a combinationthereof from the initial head direction during presentation of theinitial displayed image on the image display. In one example, tracking,via the head movement tracker 109, the head movement of the head of theuser includes measuring, via the inertial measurement unit 109, theinitial head direction on the horizontal axis (e.g., X axis), thevertical axis (e.g., Y axis), or the combination thereof (e.g.,transverse or diagonal movement). Tracking, via the head movementtracker 109, the head movement of the head of the user further includesmeasuring, via the inertial measurement unit 109, a successive headdirection on the horizontal axis, the vertical axis, or the combinationthereof during presentation of the initial displayed image.

Tracking, via the head movement tracker 109, the head movement of thehead of the user further includes determining the variation of headdirection based on both the initial head direction and the successivehead direction. Detecting movement of the user of the eyewear device 100further includes in response to tracking, via the head movement tracker109, the head movement of the head of the user, determining that thevariation of head direction exceeds a deviation angle threshold on thehorizontal axis, the vertical axis, or the combination thereof. Thedeviation angle threshold is between about 3° to 10°. As used herein,the term “about” when referring to an angle means±10% from the statedamount.

Variation along the horizontal axis slides three-dimensional objects,such as characters, Bitmojis, application icons, etc. in and out of thefield of view by, for example, hiding, unhiding, or otherwise adjustingvisibility of the three-dimensional object. Variation along the verticalaxis, for example, when the user looks upwards, in one example, displaysweather information, time of day, date, calendar appointments, etc. Inanother example, when the user looks downwards on the vertical axis, theeyewear device 100 may power down.

The right temple 110B includes temple body 211 and a temple cap, withthe temple cap omitted in the cross-section of FIG. 1B. Disposed insidethe right temple 110B are various interconnected circuit boards, such asPCBs or flexible PCBs, that include controller circuits for rightvisible light camera 114B, microphone(s) 130, speaker(s) 132, low-powerwireless circuitry (e.g., for wireless short-range network communicationvia Bluetooth™), high-speed wireless circuitry (e.g., for wireless localarea network communication via WiFi).

The right visible light camera 114B is coupled to or disposed on theflexible PCB 240 and covered by a visible light camera cover lens, whichis aimed through opening(s) formed in the right temple 110B. In someexamples, the frame 105 connected to the right temple 110B includes theopening(s) for the visible light camera cover lens. The frame 105includes a front-facing side configured to face outwards away from theeye of the user. The opening for the visible light camera cover lens isformed on and through the front-facing side. In the example, the rightvisible light camera 114B has an outwards facing angle of coverage 111Bwith a line of sight or perspective of the right eye of the user of theeyewear device 100. The visible light camera cover lens can also beadhered to an outwards facing surface of the right temple 110B in whichan opening is formed with an outwards facing angle of coverage, but in adifferent outwards direction. The coupling can also be indirect viaintervening components.

Left (first) visible light camera 114A is connected to the leftsee-through image display 180C of left optical assembly 180A to generatea first background scene of a first successive displayed image. Theright (second) visible light camera 114B is connected to the rightsee-through image display 180D of right optical assembly 180B togenerate a second background scene of a second successive displayedimage. The first background scene and the second background scenepartially overlap to present a three-dimensional observable area of thesuccessive displayed image.

Flexible PCB 140 is disposed inside the right temple 110B and is coupledto one or more other components housed in the right temple 110B.Although shown as being formed on the circuit boards of the right temple110B, the right visible light camera 114B can be formed on the circuitboards of the left temple 110A, the temples 125A-B, or frame 105.

FIG. 2A is a rear view of an example hardware configuration of aneyewear device 100, which includes an eye scanner 113 on a frame 105,for use in a system for determining an eye position and gaze directionof a wearer/user of the eyewear device 100. As shown in FIG. 2A, theeyewear device 100 is in a form configured for wearing by a user, whichare eyeglasses in the example of FIG. 2A. The eyewear device 100 cantake other forms and may incorporate other types of frameworks, forexample, a headgear, a headset, or a helmet.

In the eyeglasses example, eyewear device 100 includes the frame 105which includes the left rim 107A connected to the right rim 107B via thebridge 106 adapted for a nose of the user. The left and right rims107A-B include respective apertures 175A-B which hold the respectiveoptical element 180A-B, such as a lens and the see-through displays180C-D. As used herein, the term lens is meant to cover transparent ortranslucent pieces of glass or plastic having curved and flat surfacesthat cause light to converge/diverge or that cause little or noconvergence/divergence.

Although shown as having two optical elements 180A-B, the eyewear device100 can include other arrangements, such as a single optical elementdepending on the application or intended user of the eyewear device 100.As further shown, eyewear device 100 includes the left temple 110Aadjacent the left lateral side 170A of the frame 105 and the righttemple 110B adjacent the right lateral side 170B of the frame 105. Thetemples 110A-B may be integrated into the frame 105 on the respectivesides 170A-B (as illustrated) or implemented as separate componentsattached to the frame 105 on the respective sides 170A-B. Alternatively,the temples 110A-B may be integrated into temples (not shown) attachedto the frame 105.

In the example of FIG. 2A, the eye scanner 113 includes an infraredemitter 115 and an infrared camera 120. Visible light cameras typicallyinclude a blue light filter to block infrared light detection, in anexample, the infrared camera 120 is a visible light camera, such as alow-resolution video graphic array (VGA) camera (e.g., 640×480 pixelsfor a total of 0.3 megapixels), with the blue filter removed. Theinfrared emitter 115 and the infrared camera 120 are co-located on theframe 105, for example, both are shown as connected to the upper portionof the left rim 107A. The frame 105 or one or more of the left and righttemples 110A-B include a circuit board (not shown) that includes theinfrared emitter 115 and the infrared camera 120. The infrared emitter115 and the infrared camera 120 can be connected to the circuit board bysoldering, for example.

Other arrangements of the infrared emitter 115 and infrared camera 120can be implemented, including arrangements in which the infrared emitter115 and infrared camera 120 are both on the right rim 107B, or indifferent locations on the frame 105, for example, the infrared emitter115 is on the left rim 107A and the infrared camera 120 is on the rightrim 107B. In another example, the infrared emitter 115 is on the frame105 and the infrared camera 120 is on one of the temples 110A-B, or viceversa. The infrared emitter 115 can be connected essentially anywhere onthe frame 105, left temple 110A, or right temple 110B to emit a patternof infrared light. Similarly, the infrared camera 120 can be connectedessentially anywhere on the frame 105, left temple 110A, or right temple110B to capture at least one reflection variation in the emitted patternof infrared light.

The infrared emitter 115 and infrared camera 120 are arranged to faceinwards towards an eye of the user with a partial or full field of viewof the eye in order to identify the respective eye position and gazedirection. For example, the infrared emitter 115 and infrared camera 120are positioned directly in front of the eye, in the upper part of theframe 105 or in the temples 110A-B at either ends of the frame 105.

FIG. 2B is a rear view of an example hardware configuration of anothereyewear device 200. In this example configuration, the eyewear device200 is depicted as including an eye scanner 213 on a right temple 210B.As shown, an infrared emitter 215 and an infrared camera 220 areco-located on the right temple 210B. It should be understood that theeye scanner 213 or one or more components of the eye scanner 213 can belocated on the left temple 210A and other locations of the eyeweardevice 200, for example, the frame 105. The infrared emitter 215 andinfrared camera 220 are like that of FIG. 2A, but the eye scanner 213can be varied to be sensitive to different light wavelengths asdescribed previously in FIG. 2A.

Similar to FIG. 2A, the eyewear device 200 includes a frame 105 whichincludes a left rim 107A which is connected to a right rim 107B via abridge 106; and the left and right rims 107A-B include respectiveapertures which hold the respective optical elements 180A-B comprisingthe see-through display 180C-D.

FIGS. 2C-D are rear views of example hardware configurations of theeyewear device 100, including two different types of see-through imagedisplays 180C-D. In one example, these see-through image displays 180C-Dof optical assembly 180A-B include an integrated image display. As shownin FIG. 2C, the optical assemblies 180A-B includes a suitable displaymatrix 180C-D of any suitable type, such as a liquid crystal display(LCD), an organic light-emitting diode (OLED) display, a waveguidedisplay, or any other such display. The optical assembly 180A-B alsoincludes an optical layer or layers 176, which can include lenses,optical coatings, prisms, mirrors, waveguides, optical strips, and otheroptical components in any combination. The optical layers 176A-N caninclude a prism having a suitable size and configuration and including afirst surface for receiving light from display matrix and a secondsurface for emitting light to the eye of the user. The prism of theoptical layers 176A-N extends over all or at least a portion of therespective apertures 175A-B formed in the left and right rims 107A-B topermit the user to see the second surface of the prism when the eye ofthe user is viewing through the corresponding left and right rims107A-B. The first surface of the prism of the optical layers 176A-Nfaces upwardly from the frame 105 and the display matrix overlies theprism so that photons and light emitted by the display matrix impingethe first surface. The prism is sized and shaped so that the light isrefracted within the prism and is directed towards the eye of the userby the second surface of the prism of the optical layers 176A-N. In thisregard, the second surface of the prism of the optical layers 176A-N canbe convex to direct the light towards the center of the eye. The prismcan optionally be sized and shaped to magnify the image projected by thesee-through image displays 180C-D, and the light travels through theprism so that the image viewed from the second surface is larger in oneor more dimensions than the image emitted from the see-through imagedisplays 180C-D.

In another example, the see-through image displays 180C-D of opticalassembly 180A-B include a projection image display as shown in FIG. 2D.The optical assembly 180A-B includes a laser projector 150, which is athree-color laser projector using a scanning mirror or galvanometer.During operation, an optical source such as a laser projector 150 isdisposed in or on one of the temples 125A-B of the eyewear device 100.Optical assembly 180A-B includes one or more optical strips 155A-Nspaced apart across the width of the lens of the optical assembly 180A-Bor across a depth of the lens between the front surface and the rearsurface of the lens.

As the photons projected by the laser projector 150 travel across thelens of the optical assembly 180A-B, the photons encounter the opticalstrips 155A-N. When a particular photon encounters a particular opticalstrip, the photon is either redirected towards the user's eye, or itpasses to the next optical strip. A combination of modulation of laserprojector 150, and modulation of optical strips, may control specificphotons or beams of light. In an example, a processor controls opticalstrips 155A-N by initiating mechanical, acoustic, or electromagneticsignals. Although shown as having two optical assemblies 180A-B, theeyewear device 100 can include other arrangements, such as a single orthree optical assemblies, or the optical assembly 180A-B may havearranged different arrangement depending on the application or intendeduser of the eyewear device 100.

As further shown in FIGS. 2C-D, eyewear device 100 includes a lefttemple 110A adjacent the left lateral side 170A of the frame 105 and aright temple 110B adjacent the right lateral side 170B of the frame 105.The temples 110A-B may be integrated into the frame 105 on therespective lateral sides 170A-B (as illustrated) or implemented asseparate components attached to the frame 105 on the respective sides170A-B. Alternatively, the temples 110A-B may be integrated into temples125A-B attached to the frame 105.

In one example, the see-through image displays include the firstsee-through image display 180C and the second see-through image display180D. Eyewear device 100 includes first and second apertures 175A-Bwhich hold the respective first and second optical assembly 180A-B. Thefirst optical assembly 180A includes the first see-through image display180C (e.g., a display matrix of FIG. 2C or optical strips 155A-N′ and aprojector 150A). The second optical assembly 180B includes the secondsee-through image display 180D e.g., a display matrix of FIG. 2C oroptical strips 155A-N″ and a projector 150B). The successive field ofview of the successive displayed image includes, for example, an angleof view between about 15° to 30°, and more specifically 24°, measuredhorizontally, vertically, or diagonally. The successive displayed imagehaving the successive field of view represents a combinedthree-dimensional observable area visible through stitching together oftwo displayed images presented on the first and second image displays.

As used herein, “an angle of view” describes the angular extent of thefield of view associated with the displayed images presented on each ofthe left and right image displays 180C-D of optical assembly 180A-B. The“angle of coverage” describes the angle range that a lens of visiblelight cameras 114A-B or infrared camera 220 can image. Typically, theimage circle produced by a lens is large enough to cover the film orsensor completely, possibly including some vignetting (i.e., a reductionof an image's brightness or saturation toward the periphery compared tothe image center). If the angle of coverage of the lens does not fillthe sensor, the image circle will be visible, typically with strongvignetting toward the edge, and the effective angle of view will belimited to the angle of coverage. The “field of view” is intended todescribe the field of observable area which the user of the eyeweardevice 100 can see through his or her eyes via the displayed imagespresented on the left and right image displays 180C-D of the opticalassembly 180A-B. Image display 180C of optical assembly 180A-B can havea field of view with an angle of coverage between 15° to 110°, forexample 24°, and have a resolution of 480×480 pixels or greater.

FIG. 3 shows a rear perspective view of the eyewear device of FIG. 2A.The eyewear device 100 includes an infrared emitter 215, infrared camera220, a frame front 330, a frame back 335, and a circuit board 340. Itcan be seen in FIG. 3 that the upper portion of the left rim of theframe of the eyewear device 100 includes the frame front 330 and theframe back 335. An opening for the infrared emitter 215 is formed on theframe back 335.

As shown in the encircled cross-section in 4 the upper middle portion ofthe left rim of the frame, a circuit board, which is a flexible PCB 340,is sandwiched between the frame front 330 and the frame back 335. Alsoshown in further detail is the attachment of the left temple 110A to theleft temple 325A via the left hinge 126A. In some examples, componentsof the eye movement tracker 213, including the infrared emitter 215, theflexible PCB 340, or other electrical connectors or contacts may belocated on the left temple 325A or the left hinge 126A.

FIG. 4 is a cross-sectional view through the infrared emitter 215 andthe frame corresponding to the encircled cross-section 4 of the eyeweardevice of FIG. 3 . Multiple layers of the eyewear device 100 areillustrated in the cross-section of FIG. 4 , as shown the frame includesthe frame front 330 and the frame back 335. The flexible PCB 340 isdisposed on the frame front 330 and connected to the frame back 335. Theinfrared emitter 215 is disposed on the flexible PCB 340 and covered byan infrared emitter cover lens 445. For example, the infrared emitter215 is reflowed to the back of the flexible PCB 340. Reflowing attachesthe infrared emitter 215 to contact pad(s) formed on the back of theflexible PCB 340 by subjecting the flexible PCB 340 to controlled heatwhich melts a solder paste to connect the two components. In oneexample, reflowing is used to surface mount the infrared emitter 215 onthe flexible PCB 340 and electrically connect the two components.However, it should be understood that through-holes can be used toconnect leads from the infrared emitter 215 to the flexible PCB 340 viainterconnects, for example.

The frame back 335 includes an infrared emitter opening 450 for theinfrared emitter cover lens 445. The infrared emitter opening 450 isformed on a rear-facing side of the frame back 335 that is configured toface inwards towards the eye of the user. In the example, the flexiblePCB 340 can be connected to the frame front 330 via the flexible PCBadhesive 460. The infrared emitter cover lens 445 can be connected tothe frame back 335 via infrared emitter cover lens adhesive 455. Thecoupling can also be indirect via intervening components.

In an example, the processor 932 utilizes eye tracker 213 to determinean eye gaze direction 230 of a wearer's eye 234 as shown in FIG. 5 , andan eye position 236 of the wearer's eye 234 within an eyebox as shown inFIG. 6 . The eye tracker 213 is a scanner which uses infrared lightillumination (e.g., near-infrared, short-wavelength infrared,mid-wavelength infrared, long-wavelength infrared, or far infrared) tocaptured image of reflection variations of infrared light from the eye234 to determine the gaze direction 230 of a pupil 232 of the eye 234,and also the eye position 236 with respect to the see-through display180D.

FIG. 7 depicts an example of capturing visible light with cameras.Visible light is captured by the left visible light camera 114A with aleft visible light camera field of view 111A as a left raw image 758A.Visible light is captured by the right visible light camera 114B with aright visible light camera field of view 111B as a right raw image 758B.Based on processing of the left raw image 758A and the right raw image758B, a three-dimensional depth map 715 of a three-dimensional scene,referred to hereafter as an image, is generated by processor 932.

FIG. 8A illustrates an example of a camera-based compensation system 800processing an image 715 to improve the user experience of users ofeyewear 100/200 having partial or total blindness. To compensate forpartial or total blindness, the camera-based compensation 800 determinesobjects 802 in image 715, converts determined objects 802 to text, andthen converts the text to audio that is indicative of the objects 802 inthe image.

FIG. 8B is an image used to illustrate an example of a camera-basedcompensation system 800 responding to speech of a user, such asinstructions, to improve the user experience of users of eyewear 100/200having partial or total blindness. To compensate for partial or totalblindness, the camera-based compensation 800 processes speech, such asinstructions, received from a user/wearer of eyewear 100 to determineobjects 802 in image 715, such as a restaurant menu, and convertsdetermined objects 802 to audio that is indicative of the objects 802 inthe image responsive to the speech command.

A convolutional neural network (CNN) is a special type of feed-forwardartificial neural network that is generally used for image detectiontasks. In an example, the camera-based compensation system 800 uses aregion-based convolutional neural network (RCNN) 945. The RCNN 945 isconfigured to generate a convolutional feature map 804 that isindicative of objects 802 (FIG. 8A) and 803 (FIG. 8B) in the image 715created from the left and right cameras 114A-B. In one example, relevanttext of the convolutional feature map 804 is processed by a processor932 using a text to speech algorithm 950. In a second example, images ofthe convolutional feature map 804 are processed by processor 932 using aspeech to audio algorithm 952 to produce audio that is indicative ofobjects in the image based on the speech instructions. The processor 932includes a natural language processor configured to generate audioindicative of the objects 802 and 803 in the image 715.

In an example, and as will be discussed in further detail with respectto FIG. 10 below, image 715 generated from the left and right cameras114A-B, respectively, is shown to include objects 802, seen in thisexample as a cowboy on a horse in FIG. 8A. The image 715 is input to theRCNN 945 which generates the convolutional feature map 804 based on theimage 715. An example RCNN is available from Analytics Vidhya ofGurugram, Haryana, India. From the convolutional feature map 804 theprocessor 932 identifies a region of proposals in the convolutionalfeature map 804 and transforms them into squares 806. The squares 806represent a subset of the image 715 that is less than the whole image715, where the square 806 shown in this example includes the cowboy onthe horse. The region of proposal may be, for example, recognizedobjects (e.g., a human/cowboy, a horse, etc.) that are moving.

In another example, with reference to FIG. 8B, a user provides speechthat is input to eyewear 100 using microphone 130 to request certainobjects 803 in image 715 to be read aloud via speaker 132. In anexample, the user may provide speech to request a portion of arestaurant menu to be read aloud, such as daily dinner features, anddaily specials. The RCNN 945 determines portions of the image 715, suchas a menu, to identify objects 803 that correspond to the speechrequest. The processor 932 includes a natural language processorconfigured to generate audio indicative of the determine objects 803 inthe image 715. The processor may additionally track head/eye movement toidentify features such as a menu held in the hand of a wearer or asubset of the menu (e.g., the right or left side).

The processor 932 uses a region of interest (ROI) pooling layer 808 toreshape the squares 806 into a uniform size so that they can be inputinto a fully connected layer 810. A softmax layer 814 is used to predictthe class of the proposed ROI based on a fully connected layer 812 andalso offset values for a bounding box (bbox) regressor 816 from a ROIfeature vector 818.

The relevant text of the convolutional feature map 804 is processedthrough the text to speech algorithm 950 using the natural languageprocessor 932 and a digital signal processor is used to generate audiothat is indicative of the text in the convolutional feature map 804.Relevant text may be text identifying moving objects (e.g., the cowboyand the horse; FIG. 8A) or text of a menu matching a user's request(e.g., list of daily specials; FIG. 8B). An example text to speechalgorithm 950 is available from DFKI Berlin of Berlin, Germany. Audiocan be interpreted using a convolutional neural network, or it may beoffloaded to another device or system. The audio is generated using thespeaker 132 such that it is audible to the user (FIG. 2A)

FIG. 9 depicts a high-level functional block diagram including exampleelectronic components disposed in eyewear 100 and 200. The illustratedelectronic components include the processor 932, which executes the RCNN945, the text to speech algorithm 950, and a speech to audio algorithm952, and the memory 934.

Memory 934 includes instructions for execution by processor 932 toimplement functionality of eyewear 100/200, including instructions forprocessor 932 to perform RCNN 945, the text to speech algorithm 950, andthe speech to audio algorithm 952 to generate audio that is indicativeof an object(s) viewable through the optical element 180A-B and renderedin the images 715, such as shown if FIGS. 8A and 8B. Memory 934 alsoincludes instructions for execution by processor 932 to perform speechto audio for objects shown in image 715 such as shown in both FIG. 8Aand FIG. 8B to generate audio that is responsive to a speechinstruction. Processor 932 receives power from battery (not shown) andexecutes the instructions stored in memory 934, or integrated with theprocessor 932 on-chip, to perform functionality of eyewear 100/200, andcommunicating with external devices via wireless connections.

A user interface adjustment system 900 includes a wearable device, whichis the eyewear device 100 with an eye movement tracker 213 (e.g., shownas infrared emitter 215 and infrared camera 220 in FIG. 2B). Userinterface adjustments system 900 also includes a mobile device 990 and aserver system 998 connected via various networks. Mobile device 990 maybe a smartphone, tablet, laptop computer, access point, or any othersuch device capable of connecting with eyewear device 100 using both alow-power wireless connection 925 and a high-speed wireless connection937. Mobile device 990 is connected to server system 998 and network995. The network 995 may include any combination of wired and wirelessconnections.

Eyewear device 100 includes at least two visible light cameras 114A-B(one associated with the left lateral side 170A and one associated withthe right lateral side 170B). Eyewear device 100 further includes twosee-through image displays 180C-D of the optical assembly 180A-B (oneassociated with the left lateral side 170A and one associated with theright lateral side 170B). The image displays 180C-D are optional in thisdisclosure. Eyewear device 100 also includes image display driver 942,image processor 912, low-power circuitry 920, and high-speed circuitry930. The components shown in FIG. 9 for the eyewear device 100 arelocated on one or more circuit boards, for example a PCB or flexiblePCB, in the temples. Alternatively, or additionally, the depictedcomponents can be located in the temples, frames, hinges, or bridge ofthe eyewear device 100. Left and right visible light cameras 114A-B caninclude digital camera elements such as a complementarymetal-oxide-semiconductor (CMOS) image sensor, charge coupled device, alens, or any other respective visible or light capturing elements thatmay be used to capture data, including images of scenes with unknownobjects.

Eye movement tracking programming 945 implements the user interfacefield of view adjustment instructions, including, to cause the eyeweardevice 100 to track, via the eye movement tracker 213, the eye movementof the eye of the user of the eyewear device 100. Other implementedinstructions (functions) cause the eyewear device 100 to determine, afield of view adjustment to the initial field of view of an initialdisplayed image based on the detected eye movement of the usercorresponding to a successive eye direction. Further implementedinstructions generate a successive displayed image of the sequence ofdisplayed images based on the field of view adjustment. The successivedisplayed image is produced as visible output to the user via the userinterface. This visible output appears on the see-through image displays180C-D of optical assembly 180A-B, which is driven by image displaydriver 934 to present the sequence of displayed images, including theinitial displayed image with the initial field of view and thesuccessive displayed image with the successive field of view.

As shown in FIG. 9 , high-speed circuitry 930 includes high-speedprocessor 932, memory 934, and high-speed wireless circuitry 936. In theexample, the image display driver 942 is coupled to the high-speedcircuitry 930 and operated by the high-speed processor 932 in order todrive the left and right image displays 180C-D of the optical assembly180A-B. High-speed processor 932 may be any processor capable ofmanaging high-speed communications and operation of any generalcomputing system needed for eyewear device 100. High-speed processor 932includes processing resources needed for managing high-speed datatransfers on high-speed wireless connection 937 to a wireless local areanetwork (WLAN) using high-speed wireless circuitry 936. In certainexamples, the high-speed processor 932 executes an operating system suchas a LINUX operating system or other such operating system of theeyewear device 100 and the operating system is stored in memory 934 forexecution. In addition to any other responsibilities, the high-speedprocessor 932 executing a software architecture for the eyewear device100 is used to manage data transfers with high-speed wireless circuitry936. In certain examples, high-speed wireless circuitry 936 isconfigured to implement Institute of Electrical and Electronic Engineers(IEEE) 802.11 communication standards, also referred to herein as Wi-Fi.In other examples, other high-speed communications standards may beimplemented by high-speed wireless circuitry 936.

Low-power wireless circuitry 924 and the high-speed wireless circuitry936 of the eyewear device 100 can include short range transceivers(Bluetooth™) and wireless wide, local, or wide area network transceivers(e.g., cellular or WiFi). Mobile device 990, including the transceiverscommunicating via the low-power wireless connection 925 and high-speedwireless connection 937, may be implemented using details of thearchitecture of the eyewear device 100, as can other elements of network995.

Memory 934 includes any storage device capable of storing various dataand applications, including, among other things, color maps, camera datagenerated by the left and right visible light cameras 114A-B and theimage processor 912, as well as images generated for display by theimage display driver 942 on the see-through image displays 180C-D of theoptical assembly 180A-B. While memory 934 is shown as integrated withhigh-speed circuitry 930, in other examples, memory 934 may be anindependent standalone element of the eyewear device 100. In certainsuch examples, electrical routing lines may provide a connection througha chip that includes the high-speed processor 932 from the imageprocessor 912 or low-power processor 922 to the memory 934. In otherexamples, the high-speed processor 932 may manage addressing of memory934 such that the low-power processor 922 will boot the high-speedprocessor 932 any time that a read or write operation involving memory934 is needed.

Server system 998 may be one or more computing devices as part of aservice or network computing system, for example, that include aprocessor, a memory, and network communication interface to communicateover the network 995 with the mobile device 990 and eyewear device 100.Eyewear device 100 is connected with a host computer. For example, theeyewear device 100 is paired with the mobile device 990 via thehigh-speed wireless connection 937 or connected to the server system 998via the network 995.

Output components of the eyewear device 100 include visual components,such as the left and right image displays 180C-D of optical assembly180A-B as described in FIGS. 2C-D (e.g., a display such as a liquidcrystal display (LCD), a plasma display panel (PDP), a light emittingdiode (LED) display, a projector, or a waveguide). The image displays180C-D of the optical assembly 180A-B are driven by the image displaydriver 942. The output components of the eyewear device 100 furtherinclude acoustic components (e.g., speakers), haptic components (e.g., avibratory motor), other signal generators, and so forth. The inputcomponents of the eyewear device 100, the mobile device 990, and serversystem 998, may include alphanumeric input components (e.g., a keyboard,a touch screen configured to receive alphanumeric input, a photo-opticalkeyboard, or other alphanumeric input components), point-based inputcomponents (e.g., a mouse, a touchpad, a trackball, a joystick, a motionsensor, or other pointing instruments), tactile input components (e.g.,a physical button, a touch screen that provides location and force oftouches or touch gestures, or other tactile input components), audioinput components (e.g., a microphone), and the like.

Eyewear device 100 may optionally include additional peripheral deviceelements 919. Such peripheral device elements may include biometricsensors, additional sensors, or display elements integrated with eyeweardevice 100. For example, peripheral device elements 919 may include anyI/O components including output components, motion components, positioncomponents, or any other such elements described herein.

For example, the biometric components of the user interface field ofview adjustment 900 include components to detect expressions (e.g., handexpressions, facial expressions, vocal expressions, body gestures, oreye tracking), measure biosignals (e.g., blood pressure, heart rate,body temperature, perspiration, or brain waves), identify a person(e.g., voice identification, retinal identification, facialidentification, fingerprint identification, or electroencephalogrambased identification), and the like. The motion components includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The position components include location sensor components to generatelocation coordinates (e.g., a Global Positioning System (GPS) receivercomponent), WiFi or Bluetooth™ transceivers to generate positioningsystem coordinates, altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like. Suchpositioning system coordinates can also be received over wirelessconnections 925 and 937 from the mobile device 990 via the low-powerwireless circuitry 924 or high-speed wireless circuitry 936.

According to some examples, an “application” or “applications” areprogram(s) that execute functions defined in the programs. Variousprogramming languages can be employed to create one or more of theapplications, structured in a variety of manners, such asobject-oriented programming languages (e.g., Objective-C, Java, or C++)or procedural programming languages (e.g., C or assembly language). In aspecific example, a third party application (e.g., an applicationdeveloped using the ANDROID™ or IOS™ software development kit (SDK) byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™ WINDOWS® Phone, or another mobile operating systems. In thisexample, the third-party application can invoke API calls provided bythe operating system to facilitate functionality described herein.

FIG. 10 is a flowchart 1000 illustrating the operation of the eyeweardevice 100/200 and other components of the eyewear created by thehigh-speed processor 932 executing instructions stored in memory 934.Although shown as occurring serially, the blocks of FIG. 10 may bereordered or parallelized depending on the implementation.

Blocks 1002-1010 may be performed using the RCCN 945.

At block 1002, the processor 932 waits for user input or contextual dataand image capture. In a first example, the input is the image 715generated from the left and right cameras 114A-B, respectively, andshown to include objects 802 shown in FIG. 8A as a cowboy on a horse inthis example. In a second example, the input also includes speech from auser/wearer via microphone 130, such as verbal instructions to read anobject 803 in an image 715 placed in front of the eyewear 100, shown inFIG. 8B. This can include speech to read a restaurant menu or portionthereof, such as the daily features.

At block 1004, the processor 932 passes image 715 through the RCCN 945to generate the convolutional feature map 804. The processor 932 uses aconvolutional layer using a filter matrix over an array of image pixelsin image 715 and performs a convolutional operation to obtain theconvolution feature map 804.

At block 1006, the processor 932 uses the ROI pooling layers 808 toreshape a region of proposals of the convolutional feature map 804 intosquares 806. The processor is programmable to determine the shape andsize of the squares 806 to determine how many objects are processed andto avoid information overload. ROI pooling layer 808 is an operationused in object detection tasks using convolutional neural networks. Forexample, to detect the cowboy 802 on the horse in a single image 715shown in FIG. 8A in a first example, and to detect menu information 803shown in FIG. 8B in a second example. The ROI pooling layer 808 purposeis to perform max pooling on inputs of nonuniform sizes to obtainfixed-size feature maps (e.g. 7×7 units).

At block 1008, the processor 932 processes the fully connected layers810, where the softmax layer 814 uses fully connected layer 812 topredict the class of the proposed regions and the bounding box regressor816. A softmax layer is typically the final output layer in a neuralnetwork that performs multi-class classification (for example: objectrecognition).

At block 1010, the processor 932 identifies objects 802 and 803 in theimage 715 and selects relevant features such as objects 802 and 803. Theprocessor 932 is programmable to identify and select different classesof objects 802 and 803 in the squares 806, for example, traffic lightsof a roadway and the color of the traffic lights. In another example,the processor 932 is programmed to identify and select moving objects insquare 806 such as vehicles, trains, and airplanes. In another examplethe processor is programmed to identify and select signs, such aspedestrian crossings, warning signs and informational signs. In theexample shown in FIG. 8A, the processor 932 identifies the relevantobjects 802 as the cowboy and the horse. In the example shown in FIG.8B, the processor identifies the relevant objects 803 (e.g., based onuser instructions) such as the menu portions, e.g., daily dinnerspecials and daily lunch specials.

At block 1012, blocks 1002-1010 are repeated in order to identifyletters and text in the image 715. Processor 932 identifies the relevantletters and text. The relevant letters and text may be determined to berelevant, in one example, if they occupy a minimum portion of the image715, such as 1/1000 of the image or greater. This limits the processingof smaller letters and text that are not of interest. The relevantobjects, letters and text are referred to as features, and are allsubmitted to the text to speech algorithm 950.

Blocks 1014-1024 are performed by the text to speech algorithm 950 andspeech to audio algorithm 952. Text to speech algorithm 950 and speechto audio algorithm 952 process the relevant objects 802 and 803, lettersand texts received from the RCCN 945.

At block 1014, the processor 932 parses text of the image 715 forrelevant information as per user request or context. The text isgenerated by the convolutional feature map 804.

At block 1016, the processor 932 preprocesses the text in order toexpand abbreviations and numbers. This can include translating theabbreviations into text words, and numerals into text words.

At block 1018, the processor 932 performs grapheme to phoneme conversionusing a lexicon or rules for unknown words. A grapheme is the smallestunit of a writing system of any given language. A phoneme is a speechsound in a given language.

At block 1020, the processor 932 calculates acoustic parameters byapplying a model for duration and intonation. Duration is the amount ofelapsed time between two events. Intonation is variation in spoken pitchwhen used, not for distinguishing words as sememes (a concept known astone), but, rather, for a range of other functions such as indicatingthe attitudes and emotions of the speaker.

At block 1022, the processor 932 passes the acoustic parameters througha synthesizer to create sounds from a phoneme string. The synthesizer isa software function executed by the processor 932.

At block 1024, the processor 932 plays audio through speaker 132 that isindicative of features including objects 802 and 803 in image 715, aswell as letters and text. The audio can be one or more words havingsuitable duration and intonation. Audio sounds for words areprerecorded, stored in memory 934 and synthesized, such that any wordcan be played based on the distinct breakdown of the word. Intonationand duration can be stored in memory 934 as well for specific words inthe case of synthesis.

It will be understood that the terms and expressions used herein havethe ordinary meaning as is accorded to such terms and expressions withrespect to their corresponding respective areas of inquiry and studyexcept where specific meanings have otherwise been set forth herein.Relational terms such as first and second and the like may be usedsolely to distinguish one entity or action from another withoutnecessarily requiring or implying any actual such relationship or orderbetween such entities or actions. The terms “comprises,” “comprising,”“includes,” “including,” or any other variation thereof, are intended tocover a non-exclusive inclusion, such that a process, method, article,or apparatus that comprises or includes a list of elements or steps doesnot include only those elements or steps but may include other elementsor steps not expressly listed or inherent to such process, method,article, or apparatus. An element preceded by “a” or “an” does not,without further constraints, preclude the existence of additionalidentical elements in the process, method, article, or apparatus thatcomprises the element.

Unless otherwise stated, any and all measurements, values, ratings,positions, magnitudes, sizes, and other specifications that are setforth in this specification, including in the claims that follow, areapproximate, not exact. Such amounts are intended to have a reasonablerange that is consistent with the functions to which they relate andwith what is customary in the art to which they pertain. For example,unless expressly stated otherwise, a parameter value or the like mayvary by as much as ±10% from the stated amount.

In addition, in the foregoing Detailed Description, it can be seen thatvarious features are grouped together in various examples for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the claimed examplesrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, the subject matter to be protected liesin less than all features of any single disclosed example. Thus, thefollowing claims are hereby incorporated into the Detailed Description,with each claim standing on its own as a separately claimed subjectmatter.

While the foregoing has described what are considered to be the bestmode and other examples, it is understood that various modifications maybe made therein and that the subject matter disclosed herein may beimplemented in various forms and examples, and that they may be appliedin numerous applications, only some of which have been described herein.It is intended by the following claims to claim any and allmodifications and variations that fall within the true scope of thepresent concepts.

What is claimed is:
 1. Eyewear, comprising: a frame having a first sideand a second side; an optical element supported by the frame; a camerasupported by the eyewear and configured to generate an image having oneor more features; a microphone and a speaker supported by the eyewear;and an electronic processor configured to: receive a signal indicativeof speech from a user via the microphone comprising instructions toidentify the one or more features in the image; process the signal andidentify at least one feature from the one or more features in the imageby using a text to speech algorithm; identify letters and text in theimage; determine if the letters and the text are relevant; and generatean audio signal for presentation by the speaker that is indicative ofthe at least one feature in the image, wherein the processor isconfigured to use a convolutional neural network (CNN) to generate aconvolutional feature map having text that is indicative of the image,wherein the text to speech algorithm is configured to use a naturallanguage processor and a digital signal processor to generate the audiothat is indicative of the text in the convolutional feature map, whereinthe audio is generated only if the letters and text are determined to berelevant.
 2. The eyewear of claim 1, wherein the processor is configuredto use a convolutional layer to perform a convolutional operation toobtain the convolutional feature map.
 3. The eyewear of claim 2, whereinthe convolutional feature map has a region of proposals, wherein theprocessor is configured to use a region of interest (ROI) pooling layerto reshape the region of proposals into squares.
 4. The eyewear of claim3, wherein the processor is configured to selectively determine the sizeof the squares.
 5. The eyewear of claim 3, wherein the processor isconfigured to selectively determine a class of objects in the squares toprocess.
 6. The eyewear of claim 3, wherein the processor is configuredto use a softmax layer applied to the ROI pooling layer to predict aclass of proposed regions and a bounding box.
 7. The eyewear of claim 1,wherein the processor is configured to use a speech to audio algorithmconfigured to convert the convolutional feature map to the audio signal.8. A method for use with eyewear, the eyewear having a frame, an opticalelement supported by the frame, a camera coupled to the frame andconfigured to generate an image having one or more features, amicrophone and a speaker supported by the eyewear, and an electronicprocessor: receiving a signal indicative of speech from a user via themicrophone comprising instructions to identify the one or more featuresin the image; processing the signal and identify at least one featurefrom the one or more features in the image by using a text to speechalgorithm; identifying letters and text in the image; determining if theletters and the text are relevant; and generating an audio signal forpresentation by the speaker that is indicative of the at least onefeature in the image, wherein the processor uses a convolutional neuralnetwork (CNN) to generate a convolutional feature map having text thatis indicative of the image, wherein the text to speech algorithm uses anatural language processor and a digital signal processor to generatethe audio that is indicative of the text in the convolutional featuremap, wherein the audio is generated only if the letters and text aredetermined to be relevant.
 9. The method of claim 8, wherein theprocessor uses a convolutional layer to perform a convolutionaloperation to obtain the convolutional feature map.
 10. The method ofclaim 9, wherein the processor uses a region of interest (ROI) poolinglayer to reshape a region of proposals of the convolutional feature mapinto squares.
 11. The method of claim 10, wherein the processorselectively determines the size of the squares.
 12. The method of claim10, wherein the processor selectively determines a class of objects inthe squares to process.
 13. The method of claim 10, wherein theprocessor uses a softmax layer applied to the ROI pooling layer topredict a class of proposed regions and a bounding box.
 14. The methodof claim 8, wherein the processor uses a speech to audio algorithmconfigured to convert the convolutional feature map to the audio signal.15. A non-transitory computer-readable medium storing program codewhich, when executed, is operative to cause a computing device toperform the steps of: processing, by a processor of eyewear, a signalindicative of speech from a user comprising instructions to identify oneor more features in an image; processing, by the processor, the signaland identify at least one feature from the one or more features in theimage using a text to speech algorithm; identifying letters and text inthe image; determining if the letters and the text are relevant; andgenerating an audio signal that is indicative of the at least onefeature in the image, wherein the processor is configured to use aconvolutional neural network (CNN) to generate a convolutional featuremap having text that is indicative of the image, wherein the text tospeech algorithm is configured to use a natural language processor and adigital signal processor to generate the audio that is indicative of thetext in the convolutional feature map, wherein the audio is generatedonly if the letters and text are determined to be relevant.
 16. Thenon-transitory computer readable medium as specified in claim 15,wherein the program code, when executed, is operative to cause theprocessor to use a region of interest (ROI) pooling layer to reshape aregion of proposals of the image into squares.
 17. The non-transitorycomputer readable medium as specified in claim 16, wherein the programcode, when executed, is operative to cause the processor to use asoftmax layer applied to the ROI pooling layer to predict a class ofproposed regions and a bounding box.
 18. The eyewear of claim 1, whereinthe letters and the text are determined to be relevant if they occupy apredetermined minimum portion of the image or greater.
 19. The eyewearof claim 18, wherein the electronic processor is configured toselectively parse the letters and text of the image for relevantinformation responsive to a request.